The disclosed system and method provide a way to create, update, and execute dynamic goal plans. Updating a dynamic goal plan may be based on the initial sequence of actions of the goal plan as well as the corresponding states of the actions. By using a sequence to sequence model, a goal plan can still be processed when the length of the input (initial sequence of actions) differs from the length of the output (updated sequence of actions). A sequence to sequence model can determine the interdependencies between actions that can contribute to the optimal order in which actions can efficiently be performed. A single layer neural network or clustering can be used to approximate the state of a goal plan that may be capable infinite states. This approximation improves accuracy in capturing the state of a goal plan, thereby improving accuracy in predicting the future state of a system, which can help with planning (e.g., gathering resources in advance). Projects involving collaboration between virtual and/or human assistants can greatly benefit from the ability to update a dynamic goal plan in real time.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The computer implemented method of claim 1, further comprising creating, by a goal plan module, the initial goal plan.
3. The computer implemented method of claim 1, wherein the bidirectional RNN includes one of a long short-term memory (LSTM) and a gated recurrent unit (GRU).
4. The computer implemented method of claim 1, wherein converting the state of the initial goal plan into vector embeddings includes learning vector embeddings through a single layer neural network.
6. The computer implemented method of claim 1, wherein the initial goal plan includes a different number of actions from the updated goal plan.
7. The computer implemented method of claim 1, wherein the order of the actions in the initial goal plan differs from the order of the actions in the updated goal plan.
9. The non-transitory computer-readable medium storing software of claim 8, wherein the instructions further cause the one or more computers to create, by a goal plan module, the initial goal plan.
10. The non-transitory computer-readable medium storing software of claim 8, wherein the bidirectional RNN includes one of a long short-term memory (LSTM) and a gated recurrent unit (GRU).
11. The non-transitory computer-readable medium storing software of claim 8, wherein converting the state of the initial goal plan into vector embeddings includes learning vector embeddings through a single layer neural network.
13. The non-transitory computer-readable medium storing software of claim 8, wherein the initial goal plan includes a different number of actions from the updated goal plan.
14. The non-transitory computer-readable medium storing software of claim 8, wherein the order of the actions in the initial goal plan differs from the order of the actions in the updated goal plan.
16. The system of claim 15, wherein the instructions further cause the one or more computers to create, by a goal plan module, the initial goal plan.
17. The system of claim 15, wherein the bidirectional RNN includes one of a long short-term memory (LSTM) and a gated recurrent unit (GRU).
18. The system of claim 15, wherein converting the state of the initial goal plan into vector embeddings includes learning vector embeddings through a single layer neural network.
20. The system of claim 15, wherein the initial goal plan includes a different number of actions from the updated goal plan.
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February 19, 2020
December 20, 2022
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